Hierarchical association of entity records from different data systems

ABSTRACT

A system links data objects for common entities across source systems and includes at least one processor. The system compares data objects within each of a plurality of source systems to identify data objects associated with corresponding common entities. The identified data objects for each common entity within each of the plurality of source systems are linked to form a group of data objects for each common entity. The groups of data objects for each of the common entities are compared across the plurality of source systems to identify groups of data objects associated with common entities. The identified groups of data objects for common entities are linked across the plurality of source systems to form a set of data objects for each corresponding common entity. Embodiments of the present invention further include a method and computer program product for linking data objects for common entities across source systems.

BACKGROUND

1. Technical Field

Present invention embodiments relate to data integration for a pluralityof data systems, and more specifically, to hierarchical association ofentity records from different data systems, for example, aggregatingfrom individual source system entity matching to galaxy, or integratednetwork, level entity matching.

2. Discussion of the Related Art

Healthcare networks have very complicated organization structures. Anorganization typically comprises multiple source systems (e.g., a sourceof electronic medical records including electronic health records (EHR),records from a claims system, lab feed, various data sourcesimplementing the HL7 standard, patient satisfaction survey, etc.).Clinically integrated networks (CIN) or galaxies (e.g., a group oforganizations) are collections of individual healthcare systems withdata sharing agreements. Data governance restrictions may exist withinorganizations or galaxies (e.g., not all data can be shared with allproviders). Accordingly, examining and associating records of thedifferent healthcare systems with common entities may be complex,burdensome, and processing intensive (with respect to processingresources and processing time). This is typically exacerbated in thecase of data governance restrictions.

SUMMARY

According to one embodiment of the present invention, a system linksdata objects for the same or common entities across source systems andincludes at least one processor. The system compares data objects withineach of a plurality of source systems to identify data objectsassociated with corresponding common entities for entity resolution. Theidentified data objects for each common entity within each of theplurality of source systems are linked to form a group of data objectsfor each common entity. The groups of data objects for each of thecommon entities are compared across the plurality of source systems toidentify groups of data objects associated with common entities. Theidentified groups of data objects for common entities are linked acrossthe plurality of source systems to form a set of data objects for eachcorresponding common entity. Embodiments of the present inventionfurther include a method and computer program product for linking dataobjects for common entities across source systems in substantially thesame manner described above.

BRIEF DESCRIPTION OF THE DRAWINGS

Generally, like reference numerals in the various figures are utilizedto designate like components.

FIG. 1 is a diagrammatic illustration of an example computingenvironment according to an embodiment of the present invention.

FIG. 2 is a diagrammatic illustration of the data center of thecomputing environment of FIG. 1 according to an embodiment of thepresent invention.

FIG. 3 is a diagrammatic illustration of an example cloud computingenvironment for the computing environment of FIG. 1 according to anembodiment of the present invention.

FIG. 4 is a diagrammatic illustration of abstraction model layersaccording to an embodiment of the present invention.

FIG. 5 is a block diagram of a computing node according to an embodimentof the present invention.

FIG. 6 is a flow diagram of hierarchically associating patient recordsfrom different data sources with a common patient in an examplehierarchy according to an embodiment of the present invention.

FIG. 7 is a procedural flowchart illustrating a manner of hierarchicalassociation of patient records from different data sources according toan embodiment of the present invention.

FIG. 8A is a diagrammatic illustration of an example graphicalrepresentation of linked patient records.

FIGS. 8B-8F are illustrations of example connection tables for applyinga connected components process to the graphical representation of FIG.8A according to an embodiment of the present invention.

FIG. 9 is a procedural flowchart illustrating a manner of splitting agroup of associated patient records according to an embodiment of thepresent invention.

FIGS. 10A-10E are diagrammatic illustrations of example graphicalrepresentations of a group of linked patient records being split intoplural groups according to an embodiment of the present invention.

DETAILED DESCRIPTION

An organization may comprise multiple source systems (e.g., a source ofelectronic medical records including electronic health records (EHR),records from a claims system, lab feed, various data sourcesimplementing the HL7 standard, patient satisfaction survey, etc.), whileclinically integrated networks (CIN) or galaxies (e.g., a group oforganizations) are collections of individual healthcare systems withdata sharing agreements. These agreements may define restrictions on howthe data may be used, and such restrictions must be complied withaccording to an entity's data governance policies and procedures.Present invention embodiments pertain to hierarchical patient or otherentity matching (e.g., aggregating from entity matching at a sourcesystem level to galaxy level entity matching). For example,intra-organization assignments (or entity matches within anorganization) are derived from intra-source system assignments (orentity matches within individual source systems), while intra-galaxyassignments (or entity matches within a galaxy) are derived from theintra-organization assignments (or entity matches within individualorganizations). Further, inter-galaxy (e.g., universe of galaxies)assignments (or entity matches within a universe) are derived from theintra-galaxy assignments (or entity matches within individual galaxies).The hierarchical entity matching may be applied in a similar manner toany quantity of levels in a hierarchy.

Each additional level in a hierarchy (e.g., from a source system to agalaxy) provides a better representation of a person or other entity,which leads to better data integration performance. For example, whenfive source persons are matched at a single organization, theinformation from all five persons is now available for matching withpersons outside of the organization in the same galaxy. By way ofexample, each source system may provide varying information for personsor other entities (e.g., entity characteristics, clinical information,healthcare information, etc.). This information is aggregated asadditional persons or entities are matched, thereby providing additionalinformation for matching at the next hierarchical level. In other words,person assignments are treated as a hierarchy from a single sourcesystem, to an organization (collection of source systems), andultimately to a galaxy or clinically integrated network (CIN) (e.g.,collection of organizations).

Present invention embodiments provide several advantages. For example, apresent invention embodiment supports complex data governance issues ofan organization or galaxy by choosing whether or not to respect orenforce assignments in the component source systems/organizations.

Further, computer processing performance improvements are attained byconsidering resolved patients or entities from the previous level of thematching hierarchy. For example, matching metrics lower the number ofincorrectly matched entities, and decrease the number of incorrectlyunmatched entities. Moreover, hierarchical matching of a presentinvention embodiment reduces processing time of a processor byrespecting the hierarchy. The entity matching is preferably implementedin a distributed computing environment as described below, and is highlyscalable to hundreds of millions of patients or other entities. By wayof example, 150 million source persons may be processed into 95 millionresolved persons in a short time interval (e.g., a couple of hours,etc.).

An example computing environment flit use with present inventionembodiments is illustrated in FIG. 1. Computing environment 100 includesa healthcare network 105 in communication with a data center 115 over acommunications network 120 (e.g., providing a secure virtual privatenetwork (VPN)). The communications over network 120 preferably occurbetween a firewall 130 of healthcare network 105 and a firewall 135 ofdata center 115. The communications over network 120 may include anapplication stream 121 pertaining to communications for applications anda management stream 122 pertaining to communications for managing thedata. The network may be implemented by any number of any suitablecommunications media (e.g., wide area network (WAN), local area network(LAN), Internet, Intranet, etc.). Alternatively, healthcare network 105and data center 115 may be local to each other, and communicate via anyappropriate local communication medium (e.g., local area network (LAN),hardwire, wireless link, Intranet, etc.).

Healthcare network 105 includes a health data gateway 110 coupled toend-user systems 118 and one or more clinical/operational data sources125 providing various medical information (e.g., electronic healthrecords (EHR), records from a claims system, lab feed, various datasources implementing the HL7 standard, patient satisfaction survey,etc.) stored according to a source data model.

Data center 115 includes an application server cluster 140, a gatewaycontroller 145, a staging grid 150, and a factory grid 160. Health datagateway 110 of healthcare network 105 is configured to acquire data fromdata sources 125 and transmit the acquired data to gateway controller145 of data center 115. The gateway controller receives the incomingdata from the communications network and processes that data to staginggrid 150. The staging and factory grids each include a cluster ofcomputer systems to store data and perform parallel processing. By wayof example, the staging and factory grids each employ a HADOOP clusterwith a HADOOP distributed file system (HDFS).

Staging grid 150 inspects and publishes the data to factory grid 160 inaccordance with a data model employed by the factory grid. Factory grid160 includes various engines to perform desired analytics on the databased on queries received from end-user systems 118 and other end-usersystems 155 accessing data center 115 over network 120. The queries arehandled in conjunction with application server cluster 140 to producedesired results.

Referring to FIG. 2, health data gateway 110 of one or more healthcarenetworks is configured to acquire data from data sources 125 of thosehealthcare networks (e.g., ambulatory electronic health records (EHR),inpatient electronic health records (EHR), laboratory data, pharmacydata, health plan data, billing and accounting data, data warehouses,health information exchange (HIE)/HL7 data, patient portal, satisfactionsurveys, care management systems, etc.) and transmit the acquired datato gateway controller 145 of data center 115 as described above. Thehealthcare networks and/or data sources 125 form an acquisition layer210 providing data to data center 115 via health data gateway 110.

Gateway controller 145 receives the incoming data from communicationsnetwork 120 and processes that data to staging grid 150 employing datamodels of the source systems. Staging grid 150 includes a datainspection module 252, a data publishing module 254, and a publishauditing module 256 to inspect, publish, and audit the data to factorygrid 160 in accordance with the data model employed by the factory grid.

Factory grid 160 includes a data curation module 262, a patient matchingmodule 264, an indexing module 266, and various calculation/analyticengines 268. Data curation module 262 performs data curation operationsincluding mapping codes, data cleansing, and standardization, whilepatient matching module 264 performs patient matching operations todetermine records associated with the same patient. Indexing module 266performs indexing operations including combining records based onpatient matching, mappings, and application of risk models. Thecalculation/analytic engines perform the desired analytics based onqueries received from end-users from an interaction layer 230 enablingapplication server cluster 140 to provide various applications forprocessing and accessing the data (e.g., analytic applications, SQLaccess, etc.). The staging and factory grids form an aggregation andengines layer 220 to process the acquired data, while the queries arehandled by factory grid 160 in conjunction with application servercluster 140 to produce desired results for the interaction layer.

The various applications of application server cluster 140 may beprovided in a cloud environment. It is understood in advance thatalthough this disclosure includes a detailed description on cloudcomputing, implementation of the teachings recited herein are notlimited to a cloud computing environment. Rather, embodiments of thepresent invention are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

-   -   On-demand self-service: a cloud consumer can unilaterally        provision computing capabilities, such as server time and        network storage, as needed automatically without requiring human        interaction with the service's provider.    -   Broad network access: capabilities are available over a network        and accessed through standard mechanisms that promote use by        heterogeneous thin or thick client platforms (e.g., mobile        phones or other devices, laptops, and PDAs).    -   Resource pooling: the provider's computing resources are pooled        to serve multiple consumers using a multi-tenant model, with        different physical and virtual resources dynamically assigned        and reassigned according to demand. There is a sense of location        independence in that the consumer generally has no control or        knowledge over the exact location of the provided resources but        may be able to specify location at a higher level of abstraction        (e.g., country, state, or datacenter).    -   Rapid elasticity: capabilities can be rapidly and elastically        provisioned, in some cases automatically, to quickly scale out        and rapidly release to quickly scale in. To the consumer, the        capabilities available for provisioning often appear to be        unlimited and can be purchased in any quantity at any time.    -   Measured service: cloud systems automatically control and        optimize resource use by leveraging a metering capability at        some level of abstraction appropriate to the type of service        (e.g., storage, processing, bandwidth, and active user        accounts). Resource usage can be monitored, controlled, and        reported providing transparency for both the provider and        consumer of the utilized service.

Service Models are as follows:

-   -   Software as a Service (SaaS): the capability provided to the        consumer is to use the provider's applications running on a        cloud infrastructure. The applications are accessible from        various client devices through a thin client interface such as a        web browser (e.g., web-based e-mail). The consumer does not        manage or control the underlying cloud infrastructure including        network, servers, operating systems, storage, or even individual        application capabilities, with the possible exception of limited        user-specific application configuration settings.    -   Platform as a Service (Paas): the capability provided to the        consumer is to deploy onto the cloud infrastructure        consumer-created or acquired applications created using        programming languages and tools supported by the provider. The        consumer does not manage or control the underlying cloud        infrastructure including networks, servers, operating systems,        or storage, but has control over the deployed applications and        possibly application hosting environment configurations.    -   Infrastructure as a Service (IaaS): the capability provided to        the consumer is to provision processing, storage, networks, and        other fundamental computing resources where the consumer is able        to deploy and run arbitrary software, which can include        operating systems and applications. The consumer does not manage        or control the underlying cloud infrastructure but has control        over operating systems, storage, deployed applications, and        possibly limited control of select networking components (e.g.,        host firewalls).

Deployment Models are as follows:

-   -   Private cloud: the cloud infrastructure is operated solely for        an organization. It may be managed by the organization or a        third party and may exist on-premises or off-premises.    -   Community cloud: the cloud infrastructure is shared by several        organizations and supports a specific community that has shared        concerns (e.g., mission, security requirements, policy, and        compliance considerations). It may be managed by the        organizations or a third party and may exist on-premises or        off-premises.    -   Public cloud: the cloud infrastructure is made available to the        general public or a large industry group and is owned by an        organization selling cloud services.    -   Hybrid cloud: the cloud infrastructure is a composition of two        or more clouds (private, community, or public) that remain        unique entities but are bound together by standardized or        proprietary technology that enables data and application        portability (e.g., cloud bursting for load-balancing between        clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes. Referring now to FIG. 3, illustrativecloud computing environment 350 is depicted. As shown, cloud computingenvironment 350 comprises one or more cloud computing nodes 310 withwhich local computing devices used by cloud consumers, such as, forexample, personal digital assistant (PDA) or cellular telephone 354A,desktop computer 354B, laptop computer 354C, and/or automobile computersystem 354N may communicate. Nodes 310 may communicate with one another.They may be grouped (not shown) physically or virtually, in one or morenetworks, such as Private, Community, Public, or Hybrid clouds asdescribed hereinabove, or a combination thereof. This allows cloudcomputing environment 350 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 354A-N shown in FIG. 3 are intended to beillustrative only and that computing nodes 310 and cloud computingenvironment 350 can communicate with any type of computerized deviceover any type of network and/or network addressable connection (e.g.,using a web browser).

Referring now to FIG. 4, a set of functional abstraction layers providedby cloud computing environment 350 (FIG. 3) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 4 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 460 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 461;RISC (Reduced Instruction Set Computer) architecture based servers 462;servers 463; blade servers 464; storage devices 465; and networks andnetworking components 466. In some embodiments, software componentsinclude network application server software 467 and database software468.

Virtualization layer 470 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers471; virtual storage 472; virtual networks 473, including virtualprivate networks: virtual applications and operating systems 474; andvirtual clients 475.

In one example embodiment, management layer 480 may provide some or allof the functions for data center 115 described herein. Resourceprovisioning 481 provides dynamic procurement of computing resources andother resources that are utilized to perform tasks within the cloudcomputing environment. Metering and Pricing 482 provide cost tracking asresources are utilized within the cloud computing environment, andbilling or invoicing for consumption of these resources. In one example,these resources may comprise application software licenses. Security 486provides identity verification for cloud consumers and tasks, as well asprotection for data and other resources User portal 483 provides accessto the cloud computing environment for consumers and systemadministrators. Service level management 484 provides cloud computingresource allocation and management such that required service levels aremet. Service Level Agreement (SLA) planning and fulfillment 485 providepre-arrangement for, and procurement of, cloud computing resources forwhich a future requirement is anticipated in accordance with an SLA.

Workloads layer 490 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 491; software development and lifecycle management 492;virtual classroom education delivery 493; data analytics processing 494;transaction processing 495; aggregation and engines layer 220 (FIG. 2);and interaction layer 230 (FIG. 2).

Referring now to FIG. 5, a schematic of an example of a computing nodeor device 510 of computer environment 100 (e.g., health data gateway110, application server cluster 140, gateway controller 145, computingnodes of staging grid 150, computing nodes of factory grids 160, etc.)and cloud environment 350 (e.g., cloud computing node 310, etc.) isshown. The computing node or device is only one example of a suitablecomputing node for computing environment 100 and cloud computingenvironment 350 and is not intended to suggest any limitation as to thescope of use or functionality of embodiments of the invention describedherein. Regardless, computing node 510 is capable of being implementedand/or performing any of the functionality set forth herein.

In computing node 510, there is a computer system 512 which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system 512 include, but are not limitedto, personal computer systems, server computer systems, thin clients,thick clients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 512 may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Computer system 512 may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

As shown in FIG. 5, computer system 512 is shown in the form of ageneral-purpose computing device. The components of computer system 512may include, but are not limited to, one or more processors orprocessing units 516, a system memory 528, and a bus 518 that couplesvarious system components including system memory 528 to processor 516.

Bus 518 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system 512 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 512, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 528 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 530 and/or cachememory 532. Computer system 512 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 534 can be provided forreading from and writing to a nonremovable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,nonvolatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD ROM or other optical media can be provided.In such instances, each can be connected to bus 518 by one or more datamedia interfaces. As will be further depicted and described below,memory 528 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 540, having a set (at least one) of program modules 542,may be stored in memory 528 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 542 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system 512 may also communicate with one or more externaldevices 514 such as a keyboard, a pointing device, a display 524, etc.;one or more devices that enable a user to interact with computer system512; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 512 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces522. Still yet, computer system 512 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter520. As depicted, network adapter 520 communicates with the othercomponents of computer system 512 via bus 518. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 512. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc.

A manner of associating or linking records of a common patient or otherentity across different source data systems in a hierarchical fashion(e.g., via the factory grid and patient matching module 264) isillustrated in FIGS. 6-7. Initially, an example data set may bedistributed within a universe 600 among a plurality of galaxies 610.Each galaxy 610 includes a plurality of organizations 620, while eachorganization 620 includes a plurality of source systems 630. By way ofexample, each source system contains medical or other records associatedwith patients. For example, source systems 630 may correspond to sourcesystems 125 described above (FIG. 2). However, source systems 630 maycontain any types of records, and the system may identify recordsassociated with any type of desired entity (e.g., person, corporate orother business entity, healthcare or other medical related entity,healthcare provider, etc.) in substantially the same manner describedbelow.

Patient matching is initially performed for each source system 630 tomatch patient records within that source system and determine therecords associated with the same or common patient. The patient matchingprocess may employ various matching algorithms to determine for eachpatient of a source system 630 a group of records of the source systemassociated with that patient.

The patient matching process includes a data pre-processing stage, ablocking stage, a matching stage, a compaction stage, and a splittingstage. By way of example, each of these stages may be performed as arespective MapReduce job within the distributed computing environment offactory grid 160 (FIG. 2). However, present invention embodiments areenvironment agnostic and may be implemented in a non-distributedsetting.

Referring to FIG. 7, patient records of each source system 630 areprocessed to perform patient matching to produce for each source systemgroups of patient records associated with the same or common patient. Inparticular, patient records of a source system 630 are retrieved andpre-processed at step 705. This includes cleaning and standardizing datawithin the retrieved patient records. For example, the data may beanalyzed to identify: invalid social security numbers or otheridentifiers; invalid addresses and telephone numbers; and/or defaultvalues (e.g., a Birth date of 1900-01-01 (year/month/day), etc.).Further, the data of the retrieved patient records is standardized toenable accurate comparisons of the data for patient matching (e.g.,providing social security numbers in a desired format (e.g., with orwithout hyphens, providing data in corresponding fields (e.g., middlenames in a first name or other field), etc.).

The resulting clean and standardized data is evaluated to arrangerecords of potentially matching patients into blocks at step 710. Eachblock contains records of patients that are sufficiently similar inorder to compare records of those patients to each other in a pairwisefashion for associating the records with a common patient. Since theamount of patient records in the source systems is voluminous, comparingall pairs of patient records is impractical. For example, the amount ofcomparisons is proportional to the square of the number of records(e.g., N² comparisons are performed for N patient records). Thus, onetrillion comparisons are performed for one million patient records. Thistask becomes burdensome and time consuming even within a distributedcomputing environment.

Reducing the number of comparisons is necessary to enhance computerprocessing performance, reduce processing time, and reduce certain typesof errors. This is accomplished by organizing the collection of patientrecords into subsets or blocks. Each block contains patient records ofpatients that are sufficiently similar, where the records of thosepatients within the blocks are compared to reduce the overall number ofcomparisons.

The blocks are formed by comparing one or more fields of patient recordsto desired criteria. Exact matches on particular fields define blocks ofpatient records for pairwise comparisons of those records within eachblock (e.g., comparisons for social security number (SSN), medicalrecord number (MRN), enterprise master patient index (EMPI), telephonenumber, phonetic name matching, etc.). This enables the pairwisecomparisons of patient records to be limited to the patient recordswithin the blocks (which are likely to be associated with the same orcommon patient), thereby reducing the overall number of patient recordcomparisons.

Various blocking schemes (e.g., criteria or combinations of recordfields) may be utilized for the record comparisons to form the blocks ofpatient records. Thus, a single patient may be assigned to a pluralityof different blocks based on the blocking schemes utilized for thoseblocks (e.g., combination of fields utilized for comparison of thepatient records). The blocking schemes may include any quantity of anyfields of the patient records for the record comparisons to form theblocks.

Once the blocks of patient records are formed, the patient recordswithin each block are analyzed on an individual block basis to determinepatient records associated with the same or common patient at step 715.The patient records within a block are compared to each other in apairwise fashion to determine a likelihood score for the pair ofrecords. The comparisons are limited to the patient records within theindividual blocks in order to reduce the overall number of recordcomparisons as described above. When the likelihood score exceeds asimilarity threshold, the patient records are linked and associated withthe same patient.

In particular, the likelihood score is computed for a pair of patientrecords in a block to indicate the likelihood that the two patientrecords are associated with the same or common patient. Thedetermination of the likelihood score for a pair of patient records isbased on evidence from various features or record fields (e.g., firstname, middle name/initial, last name, gender, birth year, birthmonth/day, social security number (SSN), medical record number (MRN),enterprise master patient index (EMPI), address, postal code, telephonenumber, etc.). Each feature is associated with different matching levelsfor a comparison. By way of example:

-   -   a name feature (e.g., first name, middle name, last name, etc.)        may be associated with matching levels of exact match, alias        (nickname) match, phonetic match, typographical (error) match,        and a transpose match;    -   a birth date feature may be associated with matching levels of        exact match, typographical match and a transpose match;    -   a patient identifiers feature may be associated with a matching        level of an exact match for social security number (SSN),        medical record number (MRN), enterprise master patient index        (EMPI), etc.;    -   a telephone number feature may be associated with matching        levels of an exact match and a typographical match;    -   an address feature may be associated with matching levels of an        exact match, a typographical match, and a distance match (e.g.,        addresses within a threshold quantity of miles, etc.); and    -   an electronic (Email) mail address feature may be associated        with matching levels of an exact match, and a typographical        match.

Each matching level for a feature comparison has a correspondingassociated weight. The associated weights for the matching levelsindicate a likelihood the patient records are associated with the sameor common patient based on the level of matching of the correspondingfeature or record field. The weights for the matches may be added toproduce the likelihood score. When the likelihood score is greater thanthe similarity threshold, the patient records are linked and associatedwith the same patient. If the likelihood score is less than a differencethreshold, the patient records are split or disassociated with oneanother (since the patient records reside in the same block). By way ofexample, higher valued weights (and hence, a higher likelihood score)may indicate a greater likelihood of the patient records beingassociated with the same or common patient. However, the magnitude ofthe weight values (and likelihood score) may be associated with anydesired degree of likelihood of association of the patient records witha common patient (e.g., greater likelihood, less likelihood, etc.). Inaddition, the similarity and difference thresholds may be set to anydesired values to control the sensitivity or degree of matching neededto associate or link patient records with a common patient.

Patient matching compares patient records within a block to each otherin a pairwise, fashion, and links records together based on the resultof the comparison. Thus, various pairs of patient records may be linkedto one another based on the comparisons within the blocks. For example,a patient record within a plurality of blocks may be linked to multiplerecords from among those plurality of different blocks. The linkedpatient records are processed to transform pairs of linked patientrecords into groups of patient records 640 (FIG. 6) associated with acommon patient at step 720.

The linked pairs of patient records may be represented graphically(FIGS. 8A-8F), and combined or compacted to form groups of patientrecords with each group associated with a corresponding patient. In thiscase, a connected components process is employed on the graphicalrepresentation of the linked patient records to convert the graphicalrepresentation into all disjoint subgraphs in order to determine thegroups.

The connected components process is iterative and passes informationpertaining to connectedness throughout the graphical representation. Byway of example, a graphical representation 800 of linked patient recordsis illustrated in FIG. 8A. The graphical representation includesvertices representing patient records 1-9. The edges between thevertices indicate a link between patient records based on the patientmatching described above.

The links or connections between vertices or patient records areindicated by a table 820 a (FIG. 8B). The table includes, by way ofexample, a row for each vertex or patient record and columns for: thevertex identifier (e.g., 1-9 as shown in FIG. 8A); vertex identifiersfor adjacent vertices; an assigned group identifier; and an activatedstatus (e.g., True or False). Initially, each vertex has an assignedgroup identifier set to a minimum of the vertex identifier of thatvertex and the vertex identifiers of adjacent vertices.

Table 820 a indicates the initial connections, assigned groupidentifiers, and activated status for graphical representation 800. Forexample: vertex 1 is connected to vertices 2, 3, and has a groupidentifier set to the minimum vertex identifier of 1; vertex 2 isconnected to vertices 1, 4, and has a group identifier set to theminimum vertex identifier of 1; vertex 3 is connected to vertex 1, andhas a group identifier set to the minimum vertex identifier of 1; vertex4 is connected to vertex 2, and has a group identifier set to theminimum vertex identifier of 2; vertex 5 is connected to vertex 6, andhas a group identifier set to the minimum vertex identifier of 5; vertex6 is connected to vertices 5, 7, and has a group identifier set to theminimum vertex identifier of 5; vertex 7 is connected to vertices 6, 8,and has a group identifier set to the minimum vertex identifier of 6;vertex 8 is connected to vertices 7, 9, and has a group identifier setto the minimum vertex identifier of 7; and vertex 9 is connected tovertex 8, and has a group identifier set to the minimum vertexidentifier of 8. In addition, the activated status for each vertex isinitially set to True.

Each activated vertex (e.g., a vertex with an activated status of True)passes the assigned group identifier of that vertex to correspondingadjacent vertices. The assigned group identifier for each of theadjacent vertices is updated to be the minimum of the current assignedgroup identifier for that adjacent vertex and the group identifierspassed to that adjacent vertex. By way of example with respect tographical representation 800: the assigned group identifier for vertex 4is updated from group identifier 2 to group identifier 1 (e.g., minimumof vertex group identifier 2 and passed group identifier 1 (from vertex2)); the assigned group identifier for vertex 7 is updated from groupidentifier 6 to group identifier 5 (e.g., minimum of vertex groupidentifier 6 and passed group identifiers 5 (from vertex 6) and 7 (fromvertex 8)); the assigned group identifier for vertex 8 is updated fromgroup identifier 7 to group identifier 6 (e.g., minimum of vertex groupidentifier 7 and passed group identifiers 6 (from vertex 7) and 8 (fromvertex 9)); and the assigned group identifier for vertex 9 is updatedfrom group identifier 8 to group identifier 7 (e.g., minimum of vertexgroup identifier 8 and passed group identifier 7 (from vertex 8)). Inaddition, vertices with changed group assignments have an activatedstatus set to True, while remaining vertices have an activated statusset to False. These changes for iteration of the process) are reflectedin connection table 820 b (FIG. 8C).

Each activated vertex (e.g., a vertex with an activated status of True)from table 820 b passes the assigned group identifier of that vertex tocorresponding adjacent vertices. The assigned group identifier for eachof the adjacent vertices is updated to be the minimum of the currentassigned group identifier for that adjacent vertex and the groupidentifiers passed to that adjacent vertex. By way of example withrespect to graphical representation 800 and table 820 b: the assignedgroup identifier for vertex 8 is updated from group identifier 6 togroup identifier 5 (e.g., minimum of vertex group identifier 6 andpassed group identifiers 5 (from vertex 7) and 7 (from vertex 9)); andthe assigned group identifier for vertex 9 is updated from groupidentifier 7 to group identifier 6 (e.g., minimum of vertex groupidentifier 7 and passed group identifier 6 (from vertex 8)). Inaddition, vertices with changed group assignments have an activatedstatus set to True, while remaining vertices have an activated statusset to False. These changes (or iteration of the process) are reflectedin connection table 820 c (FIG. 8D).

Each activated vertex (e.g., a vertex with an activated status of True)from table 820 c passes the assigned group identifier of that vertex tocorresponding adjacent vertices. The assigned group identifier for eachof the adjacent vertices is updated to be the minimum of the currentassigned group identifier for that adjacent vertex and the groupidentifiers passed to that adjacent vertex. By way of example withrespect to graphical representation 800 and table 820 c: the assignedgroup identifier for vertex 9 is updated from group identifier 6 togroup identifier 5 (e.g., minimum of vertex group identifier 6 andpassed group identifier 5 (from vertex 8)). In addition, vertices withchanged group assignments have an activated status set to True, whileremaining vertices have an activated status set to False. These changes(or iteration of the process) are reflected in connection table 820 d(FIG. 8E)

Each activated vertex (e.g., a vertex with an activated status of True)from table 820 d passes the assigned group identifier of that vertex tocorresponding adjacent vertices. Since no further changes to assignedgroup identifiers occurs, all vertices are inactive (e.g., activatedstatus set to False) and the process has converged. The final groups arereflected in table 820 e (FIG. 8F), where graphical representation 800includes a group 805 of vertices 1-4 with an assigned group identifierof 1, and a group 810 of vertices 5-9 with an assigned group identifierof 5. The corresponding patient records within each group are linkedtogether to indicate the association with the same or common patient.

The patient records within each resulting group are subsequentlyexamined at step 723 to split that group into a plurality of groups asillustrated in FIG. 9. This analysis compensates for errors due totransitivity (e.g., patient record A is linked to patient record C sincepatient record A is linked to patient record B and patient record B islinked to patient record C). For example, two patient records within agroup may have different social security numbers. In this case, thegroup would be split into different groups separating the two patientrecords (and corresponding patients).

Initially, all patient records within a group are compared in a pairwisefashion at step 905 to produce a likelihood score for the pair asdescribed above. For example, weights are associated for matching levelsof features, and indicate a likelihood the patient records areassociated with the same or common patient. The weights for matchesbetween the pair of patient records may be added to produce thelikelihood score based on the level of matching of the correspondingfeatures or record fields. The resulting likelihood scores for thepatient records are compared to a splitting threshold to identify pairsof patient records with a likelihood score below the splitting thresholdat step 910.

The group of patient records may be represented graphically, wherevertices represent the patient records and each edge between a pair ofvertices or patient records is associated with a correspondinglikelihood score for that pair. Initially, the graphical representationincludes edges between each pair of patient records in the group. Theedge between patient records associated with the lowest likelihood scoreis removed from the graphical representation at step 915. The graphicalrepresentation is examined to determine whether the records of each ofthe identified pairs of patient records (with a likelihood score belowthe splitting threshold) remain connected after removal of the edge.This may be accomplished by applying a conventional or other shortestpath algorithm to determine a shortest path between the patient recordsof the identified pairs. When records of at least one of the identifiedpairs of patient records remain connected within the graphicalrepresentation as determined at step 920 (e.g., a shortest path can bedetermined between the patient records of at least one identified pair),the above process is repeated from step 915 by removing the edge betweenpatient records associated with the next lowest likelihood score. Theprocess continues until no connections exist in the graphicalrepresentation between the records of each of the identified pairs ofpatient records.

Thus, the splitting process iteratively breaks the weakest edges (e.g.,associated with the lowest likelihood scores) until there are noconnections between the identified pairs of patient records withlikelihood scores below the splitting threshold. In other words, thereare no edges in the graphical representation of the group withlikelihood scores below the splitting threshold. The resulting groupsafter the removal of edges are provided as the groups of patient records640 associated with common patients.

By way of example and referring to FIGS. 10A-10E, a group may includefour patient records (e.g., patient record 1 to patient record 4), wherethe likelihood score for patient records 1 and 3, and patient records 1and 4 are below the splitting threshold. Initially, the graphicalrepresentation 1000 of the group (FIG. 10A) includes edges between eachpair of vertices or records in the group (e.g., between patient record 1and patient records 2, 3, and 4; between patient record 2 and patientrecords 3 and 4; and between patient record 3 and patient record 4). Theedge between patient records 1 and 3 in the graphical representation ofthe group is initially removed (since this pair is associated with thelowest likelihood score) (FIG. 10B), and a shortest path algorithm isutilized to determine whether connections remain between patient record1 and patient records 3 and 4 (e.g., with likelihood scores below thesplitting threshold). Since connections remain between patient record 1and patient records 3 and 4, the process is repeated by iterativelyremoving the edges (which are associated with the next lowest likelihoodscores in this example) between patient records 1 and 3 (FIG. 10C),between patient records 2 and 4 (FIG. 10D), and between patient records2 and 3 (FIG. 10E) to produce a resulting graphical representation (FIG.10E) with no connections between patient record 1 and patient records 3and 4 (e g., associated with likelihood scores below the splittingthreshold). The resulting graphical representation provides disjointgroups including a first group with patient records 1 and 2, and asecond group with patient records 3 and 4, where each group isassociated with a different patient.

Once the resulting groups 640 are formed, the presence of remaininglevels for the hierarchy is determined at step 725. When additionallevels of the hierarchy remain, the resulting groups 640 are set as theinput to the patient matching process for the next level at step 730. Inparticular, the resulting groups 640 for each source system 630 withinthe same organization 620 are processed to perform patient matchingamong those groups in substantially the same manner described above forFIG. 7 to determine sets of records 650 associated with the samepatients. The sets of records each include the records from thecorresponding groups of the source systems and the patient matching mayemploy various matching algorithms that utilize the information from allthe records in the groups.

In particular, patient groups 640 of each source system 630 areprocessed to perform patient matching to determine sets of records 650associated with the same patients. Patient groups 640 of a source system630 are retrieved and pre-processed at step 705. This includes cleaningand standardizing data within the patient records of the retrievedpatient groups. For example, the data may be analyzed to identifyinvalid social security numbers or other identifiers; invalid addressesand telephone numbers; and/or default values (e.g., a Birth date of1900-01-01 (year/month/day), etc.) as described above. Further, the dataof the retrieved groups of patient records is standardized to enableaccurate comparisons of the data for patient matching (e.g., providingsocial security numbers in a desired format (e.g., with or withouthyphens, providing data in corresponding fields (e.g., middle names in afirst name or other field), etc.) as described above. The cleaning andstandardizing may be customized for each level based on the comparisons(e.g., individual patient records, groups of patient records, etc.), andmay even be bypassed in certain instances since the data should havealready been cleansed and standardized at the prior level.

The resulting clean and standardized data is evaluated to assign groupsof potentially matching patients to blocks at step 710. Each blockcontains groups of patient records 640 that are sufficiently similar inorder to compare groups to each other in a pairwise fashion forassociating the groups with a common patient.

Each block contains groups 640 (of patient records) that aresufficiently similar, where the groups within the blocks are compared.The blocks are formed by comparing one or more fields of patient recordswithin the groups 640 to certain criteria. Exact matches on particularfields define smaller blocks of groups 640 for pairwise comparisons ofthose groups. This enables the pairwise comparisons to be limited to thegroups 640 within the blocks (which are likely to be associated with thesame or common patient), thereby reducing the overall number of patientrecord comparisons.

Various blocking schemes (e.g., criteria or combinations of recordfields) may be utilized for the group comparisons to form the blocks ofgroups 640. Thus, a single group may be assigned to a plurality ofdifferent blocks based on the blocking schemes utilized for those blocks(e.g., combination of fields utilized for comparison of the patientrecords). The blocking schemes may include any quantity of any fields ofthe patient records within the groups for the comparisons to form theblocks. Further, since a group may contain a plurality of patientrecords from different sources, additional information (relative to aprior hierarchical level) is available to use as criteria for theblocking schemes. For example, patient records within a group mayinclude variations within the same record field (e.g., a first namefield among the patient records within a group may include a nickname,an abbreviated name or a formal name). A blocking scheme may include anyquantity of the variants to form the block (e.g., a block may be formedbased on groups with a first name field having a certain nickname,etc.).

Once the blocks of groups 640 are formed, the groups within each blockare analyzed on an individual block basis to determine sets of groups(or patient records) associated with the same or common patient at step715. The groups 640 within a block are compared to each other in apairwise fashion to determine a likelihood score for the pair of groups.The comparisons are limited to the groups within the individual blocksin order to reduce the overall number of record comparisons as describedabove. When the likelihood score exceeds a similarity threshold, thepatient groups are linked and associated with the same patient.

In particular, the likelihood score is computed for a pair of groups 640in a block to indicate the likelihood that the two groups of patientrecords are associated with the same or common patient. Thedetermination of the likelihood score for a pair of groups is based onevidence from various features or record fields (e.g., first name,middle name/initial, last name, gender, birth year, birth month/day,social security number (SSN), medical record number (MRN), enterprisemaster patient index (EMPI), address, postal code, telephone number,etc.) as described above.

Each feature has different matching levels, where each matching levelfor a feature has a corresponding associated weight as described above.The associated weights for the matching levels indicate a likelihood thegroups 640 are associated with the same or common patient based on thelevel of matching of the corresponding feature or record field betweenthe groups. Since a group may contain a plurality of patient records,the record fields may have varying values for the same field asdescribed above. In this case, the comparisons may be expanded toinclude the variants (or different record field values) from the groups.The maximum weight from a comparison of the feature or record fieldbetween records of each of the groups (e.g., in pairwise comparisons ofrecords between the groups) is determined and used as the weight forthat feature. For example, a highest attained matching level (associatedwith the greatest weight value) may be utilized for a particularfeature.

Since groups may contain patient records from different sources, thegroups may contain additional information or evidence (relative to theprior hierarchical level) that may be utilized to match the groups.Thus, each additional level in the hierarchy (from source system togalaxy) provides a better representation of a patient that leads tobetter performance. For example, when five patients at a single sourcesystem 630 get matched together, the information available from the fivepatients is now available to for blocking and/or matching with patientsin other source systems 630 in the same organization 620. Accordingly,the blocking and patient matching criteria may be configured for eachhierarchical level to account for additional information.

The weights for the features may be added to produce the likelihoodscore for the pair of groups. When the likelihood score is greater thana similarity threshold, the groups are linked and associated with thesame patient. If the likelihood score is less than a differencethreshold, the groups are split or disassociated with one another (sincethey reside in the same block). By way of example, higher valued weights(and hence, a higher likelihood score) may indicate a greater likelihoodof the groups being associated with the same or common patient. However,the magnitude of the matching level and weight values (and likelihoodscore) may be associated with any desired degree of likelihood ofassociation of the groups with a common patient (e.g., greaterlikelihood, less likelihood, etc.). In addition, the similarity anddifference thresholds may be set to any desired values to control thesensitivity or degree of matching needed to associate or link groupswith a common patient.

The patient matching compares groups of patient records within a blockto each other in a pairwise fashion, and links the groups together basedon the result of the comparison. Thus, various pairs of groups 640 maybe linked to one another based on the comparisons within the blocks. Forexample, a group within a plurality of blocks may be linked to multiplegroups from among those plurality of different blocks. The linked groupsare processed to transform pairs of linked groups into sets of groups650 (FIG. 6) associated with a common patient at step 720.

The linked pairs of patient groups may be represented graphically (e.g.,similar to FIGS. 8A-8F, but with the vertices representing groups), andcombined or compacted to form sets of groups with each set associatedwith a corresponding patient. In this case, a connected componentsprocess is employed on the graphical representation of the linkedpatient groups in substantially the same manner described above toconvert the graphical representation into all disjoint subgraphs inorder to determine the sets. The connected components process isiterative and passes information pertaining to connectedness throughoutthe graphical representation as described above.

The patient groups within each resulting set are subsequently examinedto determine whether to split the set into a plurality of sets insubstantially the same manner described above (FIG. 9). In this case,the likelihood scores between the pairs of groups in a set aredetermined in substantially the same manner described above for grouppatient matching (FIG. 7). The vertices in the graphical representationof the set represent the patient groups, and the edges between thepatient groups are associated with the likelihood scores. This analysiscompensates for errors due to transitivity (e.g., patient group A islinked to patient group C since patient group A is linked to patientgroup and patient group B is linked to patient group C).

The splitting process iteratively breaks the weakest edges (e g.,associated with the lowest likelihood scores between the groups) untilthere are no connections between the identified pairs of patient groupswith likelihood scores below the splitting threshold. In other words,there are no edges in the graphical representation of the set withlikelihood scores below the splitting threshold. The resulting setsafter the removal of edges are provided as the sets of patient records650 associated with common patients as described above.

Once the resulting sets 650 are formed, the presence of remaining levelsfor the hierarchy is determined at step 725. At this point, each set ofgroups 650 represents records across (source systems 630 of) anorganization 620 associated with a same corresponding patient. Whenadditional levels of the hierarchy remain, the resulting sets 650 areset as the input to the patient matching process for the next level atstep 730.

Subsequent levels are processed in substantially the same mannerdescribed above, where the resulting grouped patient records from theprior level are utilized as input to the succeeding level at step 730.The grouped patient records from the prior level are processed at thenext level similar to the patient record (or group) as described above.In these cases, the vertices of the graphical representations for asucceeding level represent the grouped patient records from the priorlevel. Each additional level in the hierarchy (from source system togalaxy) provides a better representation of a person, which leads tobetter data integration and computer performance. For example, when aplurality of patients are matched at an organization, the informationavailable from all the matched patients is now available to match themwith patients outside of the organization in the same galaxy.

In addition, the source systems may store protected health (PHI) orother protected or confidential information. The matching processdescribed above may control the amount of protected information exposedat every hierarchical level of matching for compliance with datagovernance or other restrictions. For example, the initial hierarchicallevels (e.g., patient records or groups within the same organization)may use all data, but the protected information may be restricted forsome of the higher hierarchical levels (e.g., galaxies or universeacross different organizations). Thus, the hierarchical patient matchingprocess enables control of protected information for data governancepurposes and to comply with data governance or other policies. Theamount of protected information may be specified in the configurationfor a hierarchical level (e.g., as part, of the blocking and/or matchingcriteria, additional criteria, etc.).

In particular, the resulting sets 650 for each organization 620 withinthe same galaxy 610 are processed in substantially the same mannerdescribed above (FIG. 7) to determine collections of records 660associated with the same patients. The collections of records 660 eachinclude the records from the corresponding sets 650 of the organization620, and the blocking and patient matching may employ various blockingschemes and matching algorithms that utilize the information from allthe records in the sets. Since the sets of records 650 typically includeplural groups of patient records the blocking schemes and matchingalgorithms may employ the techniques described above for groups ofpatient records (e.g., maximum level of matching for a feature, blockingscheme criteria, etc.) to process a set of records 650 as a unit (e.g.,for the blocking, matching, compacting, and splitting stages). Inaddition, the vertices of the graphical representations for determiningcollections of records 660 represent sets 650 from the prior level(e.g., for the compaction and splitting stages). At this point, eachcollection 660 represents records across (series of source systems ororganizations 620 of) a galaxy 610 associated with a same correspondingpatient.

The resulting collections 660 for each galaxy 610 are processed insubstantially the same manner described above to determine resultinggroupings of records 670 associated with the same patients. Theresulting groupings of records each include the records from thecorresponding collections 660 of galaxies 610, and the blocking andpatient matching may employ various blocking schemes and matchingalgorithms that utilize the information from all the records in thecollections. Since the collections of records 660 typically includeplural sets of patient records, the blocking schemes and matchingalgorithms may employ the techniques described above for groups ofpatient records (e.g., maximum level of matching for a feature, blockingscheme criteria, etc.) to process a collection of records 660 as a unit(e.g., for the blocking, matching, compacting, and splitting stages). Inaddition, the vertices of the graphical representations for determiningresulting groupings 670 represent collections of records 660 from theprior level (e.g., for the compaction and splitting stages). At thispoint, each resulting grouping 670 represents records across clusters ofseries of source systems or galaxies 610 (of universe 600) associatedwith a same corresponding patient.

The process terminates when no further hierarchical levels remain forprocessing as determined at step 725. The process may be performed forany hierarchy including any quantity of levels.

Thus, patient matching assignments or groupings 670 for galaxies 610 areconstructed or aggregated from single organization patient matchingassignments or groupings 640, 650. Assignments from an organization 620are respected within a galaxy 600 (e.g., groupings 640, 650 and 660 areprocessed as a unit and not modified at subsequent levels), and eachorganization 620 and galaxy 600 may be associated with a specificpatient matching configuration (e.g., blocking or matching criteria;similarity, difference, and splitting thresholds; amount of confidentialinformation permitted/restricted; indication of permitted/restrictedinformation; etc.) which is useful for data governance. The patientmatching process described above (FIG. 7) is performed for upperhierarchical levels or galaxies 610 in substantially the same manner aslower hierarchical levels or source systems (e.g., patient records)except that the inputs being processed by the higher hierarchical levelsare groupings of patient records (which are processed as a unit) fromthe preceding hierarchical level (e.g., groupings 640, 650, 660) ratherthan single patient records.

The patient matching processes described above (e.g., pre-process,blocking, matching, compaction, and splitting) are typically executed inbatch at off-peak periods (e.g., a nightly basis, etc). Further, theseprocesses are executed in a distributed computing environment (e.g.,factory grid 160) and can easily scale to hundreds of millions ofpatients or other entities. A data lineage (e.g., identifying the sourcesystem, organization, galaxy, etc.) is maintained throughout the patientmatching process, where patient assignments (or groupings) can betracked over time. Thus, source identifiers (e.g., indicating the sourcesystems) can be retrieved and assigned patient identifiers that arestable over time. The patient matching processes are preferablyperformed in the off-peak processing, where downstream analytics arepreferably performed on an intra-organization level rather than a sourcesystem level.

It will be appreciated that the embodiments described above andillustrated in the drawings represent only a few of the many ways ofimplementing embodiments for hierarchical association of entity recordsfrom different data systems.

The environment of the present invention embodiments may include anynumber of computer or other processing systems (e.g., client or end-usersystems, server systems, etc.) and databases or other repositoriesarranged in any desired fashion, where the present invention embodimentsmay be applied to any desired type of computing environment (e.g., cloudcomputing, client-server, network computing, mainframe, stand-alonesystems, etc.). The computer or other processing systems employed by thepresent invention embodiments may be implemented by any number of anypersonal or other type of computer or processing system (e.g., desktop,laptop, PDA, mobile devices, etc.), and may include any commerciallyavailable operating system and any combination of commercially availableand custom software (e.g., browser software, communications software,server software, patient matching module, etc.). These systems mayinclude any types of monitors and input devices (e.g., keyboard, mouse,voice recognition, etc.) to enter and/or view information.

It is to be understood that the software (e.g., patient matching module,etc.) of the present invention embodiments may be implemented in anydesired computer language and could be developed by one of ordinaryskill in the computer arts based on the functional descriptionscontained in the specification and flow charts illustrated in thedrawings. Further, any references herein of software performing variousfunctions generally refer to computer systems or processors performingthose functions under software control. The computer systems of thepresent invention embodiments may alternatively be implemented by anytype of hardware and/or other processing circuitry.

The various functions of the computer or other processing systems may bedistributed in any manner among any number of software and/or hardwaremodules or units, processing or computer systems and/or circuitry, wherethe computer or processing systems may be disposed locally or remotelyof each other and communicate via any suitable communications medium(e.g., LAN, WAN, Intranet, Internet, hardwire, modem connection,wireless, etc.). For example, the functions of the present inventionembodiments may be distributed in any manner among the variousend-user/client and server systems, and/or any other intermediaryprocessing devices. The software and/or algorithms described above andillustrated in the flow charts may be modified in any manner thataccomplishes the functions described herein. In addition, the functionsin the flow charts or description may be performed in any order thataccomplishes a desired operation.

The software of the present invention embodiments (e.g., patientmatching module, etc.) may be available on a non-transitory computeruseable medium (e.g., magnetic or optical mediums, magneto-opticmediums, floppy diskettes, CD-ROM, DVD, memory devices, etc.) of astationary or portable program product apparatus or device for use withstand-alone systems or systems connected by a network or othercommunications medium.

The communication network may be implemented by any number of any typeof communications network (e.g., LAN, WAN, Internet, VPN, etc.). Thecomputer or other processing systems of the present inventionembodiments may include any conventional or other communications devicesto communicate over the network via any conventional or other protocols.The computer or other processing systems may utilize any type ofconnection (e.g., wired, wireless, etc.) for access to the network.Local communication media may be implemented by any suitablecommunication media local area network (LAN), hardwire, wireless link,Intranet, etc.).

The system may employ any number of any conventional or other databases,data stores or storage structures (e.g., files, databases, datastructures, data or other repositories, etc.) to store information(e.g., blocking or matching criteria, thresholds, etc.). The databasesystem may be implemented by any number of any conventional or otherdatabases, data stores or storage structures (e.g., files, databases,data structures, data or other repositories, etc.) to store information.The database system may be included within or coupled to the serverand/or client systems. The database systems and/or storage structuresmay be remote from or local to the computer or other processing systems,and may store any desired data.

The present invention embodiments may utilize data in any desiredstructure (e.g., records, data objects, data structures, etc.), andassociate the data with any desired entity (e.g., person, corporate orother business entity, healthcare or other medical related entity,healthcare provider, etc.). Present invention embodiments may be appliedto any hierarchical structure having any quantity of levels.

The blocking scheme may arrange any quantity or unit of records (e.g.,individual records, groups of records, sets of groups, collections ofsets, etc.) into blocks or sub-groups for comparisons based on anydesired criteria (e.g., any type of matching of any portion of anyquantity of record fields, based on record characteristics, etc.). Anyquantity of records within a unit (e.g., group of records, sets ofgroups, collection of sets, etc.) may match the criteria in any fashionto qualify for a block. The similarity and difference thresholds may beset to any desired values.

The matching process may compare or match any quantity or unit ofrecords (e.g., individual records, groups of records, sets of groups,collections of sets, etc.) based on any desired criteria (e.g., anyquantity or combination of record fields or features, etc.). Thelikelihood score may include any quantity of any types of levels ofmatching (e.g., exact, partial, phonetic, typo, etc.). The weights maybe assigned to the matching levels in any desired fashion, and includeany values. The weights may be combined in any fashion to provide alikelihood score. The value of the likelihood score may be associatedwith any degree of similarity (e.g., a lesser likelihood score mayindicate a greater chance for a match, a greater likelihood score mayindicate a greater chance for a match, etc.).

Any quantity of records within a unit of records (e.g., group ofrecords, sets of groups, collection of sets, etc.) may match to providea match for the unit (e.g., any quantity of records for a group, anyquantity of groups for a set, any quantity of sets for a collection,etc.). Further, the matching criteria may be expanded in any fashion toaccount for additional information at the hierarchical levels (e.g.,increase variants or values provided for matching, expand matching typesor levels, etc.). The matching level associated with any desired weight(e.g., greatest weight, lowest weight, etc.) may be selected for afeature of a plurality of records (e.g., group of records, set ofgroups, collection of sets, etc.).

The linked units (e.g., linked records, groups of records, sets ofgroups, collection of sets, etc.) may be represented graphically in anyfashion (e.g., nodes and edges or arcs, etc.). The disjoint sets withinthe graphical representation of a unit (e.g., linked records, groups ofrecords, sets of groups, collections of sets, etc.) may be identified inany fashion. The splitting may split a unit (e.g., group of records, setof groups, collection of sets, etc.) into any quantity of those unitsbased on any desired criteria (e.g., high or low likelihood values anysuitable matching criteria, etc.). The splitting threshold may be set toany desired value.

Records may be associated with the resulting units (e.g., groups ofrecords, sets of groups, collection of sets, etc.) based on any data(e.g., linking the actual records (e.g., pointer or other indicator,etc.), grouping record or other identifiers to associate the recordswith a unit, etc.). The process may include one or more from a group ofthe pre-processing, blocking, matching, compaction, and splittingstages, either individually or any combination.

The present invention embodiments may employ any number of any type ofuser interface (e.g., Graphical User interface (GUI), command-line,prompt, etc.) for obtaining or providing information (e.g., queries,analytic results, etc.), where the interface may include any informationarranged in any fashion. The interface may include any number of anytypes of input or actuation mechanisms (e.g., buttons, icons, fields,boxes, links, etc.) disposed at any locations to enter/displayinformation and initiate desired actions via any suitable input devices(e.g., mouse, keyboard, etc.). The interface screens may include anysuitable actuators (e.g., links, tabs, etc.) to navigate between thescreens in any fashion.

The present invention embodiments are not limited to the specific tasksor algorithms described above, but may be utilized for associating datafrom various data systems with any type of common entity.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a” an and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes” “including”, “has”, “have”, “having”, “with”and the like, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing, processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecompute; readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A system for linking data objects for commonentities across source systems comprising: at least one processor tocompare the data objects according to levels of a hierarchy, wherein thehierarchy includes a first level indicating record level comparisons ofindividual electronic records of the source systems and a second levelindicating group level comparisons of groups of the individualelectronic records produced from comparisons of the first level, andwherein the at least one processor is configured to: compare dataobjects within each of a plurality of source systems to each otheraccording to the first level of the hierarchy to identify on each sourcesystem data objects associated with corresponding common entities,wherein each source system stores electronic records of a plurality ofdifferent entities and at least one entity is associated with aplurality of electronic records stored on two or more different sourcesystems; link the identified data objects for each common entity withineach of the plurality of source systems to form a group of data objectsfor each common entity on each source system for comparison according tothe second level of the hierarchy, wherein the linked data objects for agroup include varying information for a corresponding common entity toenable the group to provide additional information for that commonentity relative to an individual data object; compare the groups of dataobjects for each of the common entities from the plurality of sourcesystems to each other based on a set of criteria pertaining to theadditional information and according to the second level of thehierarchy to identify groups of data objects associated with commonentities, wherein comparing the groups of data objects based on thehierarchy and additional information improves performance of the atleast one processor by reducing comparisons and processing resolvedentities to decrease numbers of incorrectly matched entities andincorrectly unmatched entities; and link the identified groups of dataobjects for common entities from the plurality of source systems to forma set of data objects for each corresponding common entity, wherein atleast one set of data objects includes two or more groups of dataobjects from different source systems.
 2. The system of claim 1, whereinthe at least one processor is further configured to: form sets of dataobjects for corresponding common entities for each of plural series ofthe plurality of source systems; compare the sets of data objects foreach of the common entities across the series to identify sets of dataobjects associated with common entities; and link the identified sets ofdata objects for common entities across the series to form a collectionof data objects for each corresponding common entity.
 3. The system ofclaim 2, wherein the at least one processor is further configured to:form collections of data objects for corresponding common entities foreach of plural clusters of a plurality of the series; compare thecollections of data objects for each of the common entities across theclusters to identify collections of data objects associated with commonentities; and link the identified collections of data objects for commonentities across the clusters to form a resulting grouping of dataobjects for each corresponding common entity.
 4. The system of claim 1,wherein the common entities include a patient.
 5. The system of claim 3,wherein the data objects include protected information, and the at leastone processor is further configured to: control an amount of theprotected information utilized for each of the comparisons of the dataobjects.
 6. The system of claim 5, wherein the protected information isaccessible based on data governance policies, and controlling an amountof the protected information further comprises: controlling the amountof the protected information utilized for each of the comparisons of thedata objects to comply with the data governance policies.
 7. The systemof claim 1, wherein the at least one processor is further configured to:split the group of data objects for a common entity into plural groupsof data objects based on similarities between the data objects in thegroup.
 8. The system of claim 1, wherein the at least one processor isfurther configured to: split the set of data objects for a common entityinto plural sets of data objects based on similarities between thegroups of data objects in the set.
 9. A computer program product forlinking data objects for common entities across source systems, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by at least one processor to cause the at least one processorto: compare data objects within each of a plurality of source systems toeach other according to a first level of a hierarchy to identify on eachsource system data objects associated with corresponding commonentities, wherein each source system stores electronic records of aplurality of different entities and at least one entity is associatedwith a plurality of electronic records stored on two or more differentsource systems, and wherein the hierarchy includes the first levelindicating record level comparisons of individual electronic records ofthe source systems and a second level indicating group level comparisonsof groups of the individual electronic records produced from comparisonsof the first level; link the identified data objects for each commonentity within each of the plurality of source systems to form a group ofdata objects for each common entity on each source system for comparisonaccording to the second level of the hierarchy, wherein the linked dataobjects for a group include varying information for a correspondingcommon entity to enable the group to provide additional information forthat common entity relative to an individual data object; compare thegroups of data objects for each of the common entities from theplurality of source systems to each other based on a set of criteriapertaining to the additional information and according to the secondlevel of the hierarchy to identify groups of data objects associatedwith common entities, wherein comparing the groups of data objects basedon the hierarchy and additional information improves performance of theat least one processor by reducing comparisons and processing resolvedentities to decrease numbers of incorrectly matched entities andincorrectly unmatched entities; and link the identified groups of dataobjects for common entities from the plurality of source systems to forma set of data objects for each corresponding common entity, wherein atleast one set of data objects includes two or more groups of dataobjects from different source systems.
 10. The computer program productof claim 9, wherein the at least one processor is further caused to:form sets of data objects for corresponding common entities for each ofplural series of the plurality of source systems; compare the sets ofdata objects for each of the common entities across the series toidentify sets of data objects associated with common entities; and linkthe identified sets of data objects for common entities across theseries to form a collection of data objects for each correspondingcommon entity.
 11. The computer program product of claim 10, wherein theat least one processor is further caused to: form collections of dataobjects for corresponding common entities for each of plural clusters ofa plurality of the series; compare the collections of data objects foreach of the common entities across the clusters to identify collectionsof data objects associated with common entities; and link the identifiedcollections of data objects for common entities across the clusters toform a resulting grouping of data objects for each corresponding commonentity.
 12. The computer program product of claim 11, wherein the dataobjects include protected information, and the at least one processor isfurther caused to: control an amount of the protected informationutilized for each of the comparisons of the data objects.
 13. Thecomputer program product of claim 12, wherein the protected informationis accessible based on data governance policies, and controlling anamount of the protected information further comprises: controlling theamount of the protected information utilized for each of the comparisonsof the data objects to comply with the data governance policies.
 14. Thecomputer program product of claim 9, wherein the common entities includea patient.
 15. The computer program product of claim 9, wherein the atleast one processor is further caused to: split the group of dataobjects for a common entity into plural groups of data objects based onsimilarities between the data objects in the group.
 16. The computerprogram product of claim 9, wherein the at least one processor isfurther caused to: split the set of data objects for a common entityinto plural sets of data objects based on similarities between thegroups of data objects in the set.